EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning
Deep transfer learning techniques try to tackle the limitations of deep learning, the dependency on extensive training data and the training costs, by reusing obtained knowledge. However, the...
A Brief Review of Domain Adaptation
Classical machine learning assumes that the training and test sets come from the same distributions. Therefore, a model learned from the labeled training data is expected to perform well on the...
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities
Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks...
A Concise Review of Transfer Learning
The availability of abundant labeled data in recent years led the researchers to introduce a methodology called transfer learning, which utilizes existing data in situations where there are...
Sarcasm Detection: A Comparative Study
Sarcasm detection is the task of identifying irony containing utterances in sentiment-bearing text. However, the figurative and creative nature of sarcasm poses a great challenge for affective...
Automatic Generation of Descriptive Titles for Video Clips Using Deep Learning
Over the last decade, the use of Deep Learning in many applications produced results that are comparable to and in some cases surpassing human expert performance. The application domains include...
Decoding the Alphabet Soup of Degrees in the United States Postsecondary Education System Through Hybrid Method: Database and Text Mining
This paper proposes a model to predict the levels (e.g., Bachelor, Master, etc.) of postsecondary degree awards that have been ambiguously expressed in the student tracking reports of the National...
DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTM
Medical Imaging is one of the growing fields in the world of computer vision. In this study, we aim to address the Diabetic Retinopathy (DR) problem as one of the open challenges in medical...
Comparing Machine Learning Techniques for Alfalfa Biomass Yield Prediction
The alfalfa crop is globally important as livestock feed, so highly efficient planting and harvesting could benefit many industries, especially as the global climate changes and traditional...
Generative Adversarial Network Applications in Creating a Meta-Universe
Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in...
The Use of Video Captioning for Fostering Physical Activity
Video Captioning is considered to be one of the most challenging problems in the field of computer vision. Video Captioning involves the combination of different deep learning models to perform...
The application of Evolutionary and Nature Inspired Algorithms in Data Science and Data Analytics
In the past 30 years, scientists have searched nature, including animals and insects, and biology in order to discover, understand, and model solutions for solving large-scale science challenges....
An Integrated Approach for Video Captioning and Applications
Physical computing infrastructure, data gathering, and algorithms have recently had significant advances to extract information from images and videos. The growth has been especially outstanding...
A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings
People with Parkinson's disease must be regularly monitored by their physician to observe how the disease is progressing and potentially adjust treatment plans to mitigate the symptoms. Monitoring...
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model
Probabilistic Graphical Models (PGM) are very useful in the fields of machine learning and data mining. The crucial limitation of those models,however, is the scalability. The Bayesian Network,...
GELATO and SAGE: An Integrated Framework for MS Annotation
Several algorithms and tools have been developed to (semi) automate the process of glycan identification by interpreting Mass Spectrometric data. However, each has limitations when annotating MSn...